Modern petroleum drilling and production operations demand a great quantity of information relating to parameters and conditions downhole. Such information typically includes characteristics of the earth formations traversed by the wellbore, in addition to data relating to the size and configuration of the borehole itself. Oil well logging has been known in the industry for many years as a technique for providing information to a formation evaluation professional or driller regarding the particular earth formation being drilled. The collection of information relating to conditions downhole, which commonly is referred to as “logging,” can be performed by several methods. These methods include measurement while drilling, MWD, and logging while drilling, LWD, in which a logging tool is carried on a drill string during the drilling process. The methods also include wireline logging.
In conventional oil well wireline logging, a probe or “sonde” is lowered into the borehole after some or all of the well has been drilled, and is used to determine certain characteristics of the formations traversed by the borehole. The sonde may include one or more sensors to measure parameters downhole and typically is constructed as a hermetically sealed cylinder for housing the sensors, which hangs at the end of a long cable or “wireline.” The cable or wireline provides mechanical support to the sonde and also provides electrical connections between the sensors and associated instrumentation within the sonde, and electrical equipment located at the surface of the well. Normally, the cable supplies operating power to the sonde and is used as an electrical conductor to transmit information signals from the sonde to the surface. In accordance with conventional techniques, various parameters of the earth's formations are measured and correlated with the position of the sonde in the borehole as the sonde is pulled uphole.
A chart or plot of an earth parameter or of a logging tool signal versus the position or depth in the borehole is called a “log.” The depth may be the distance from the surface of the earth to the location of the tool in the borehole or may be true depth, which is the same only for a perfectly vertical straight borehole. The log of the tool signal or raw data often does not provide a clear representation of the earth parameter which the formation evaluation professional or driller needs to know. The tool signal must usually be processed to produce a log which more clearly represents a desired parameter. The log is normally first created in digital form by a computer and stored in computer memory, on tape, disk, etc. and may be displayed on a computer screen or printed in hard copy form.
The sensors used in a wireline sonde usually include a source device for transmitting energy into the formation, and one or more receivers for detecting the energy reflected from the formation. Various sensors have been used to determine particular characteristics of the formation, including nuclear sensors, acoustic sensors, and electrical sensors. See generally J. Lab, A Practical Introduction to Borehole Geophysics (Society of Exploration Geophysicists 1986); D. R. Skinner, Introduction to Petroleum Production, Volume 1, at 54-63 (Gulf Publishing Co. 1981).
For a formation to contain petroleum, and for the formation to permit the petroleum to flow through it, the rock comprising the formation must have certain well-known physical characteristics. One characteristic is that the formation has a certain range of measurable resistivity (or conductivity), which in many cases can be determined by inducing an alternating electromagnetic field into the formation by a transmitter coil arrangement. The electromagnetic field induces alternating electric (or eddy) currents in the formation in paths that are substantially coaxial with the transmitter. These currents in turn create a secondary electromagnetic field in the medium, inducing an alternating voltage at the receiver coil. If the current in the transmitter coil is kept constant, the eddy current intensity is generally proportional to the conductivity of the formation. Consequently, the conductivity of the formation determines the intensity of the secondary electromagnetic field, and thus, the amplitude of the voltage at the receiver coil. See generally, James R. Jordan, et al., Well Logging II—Electric And Acoustic Logging, SPE Monograph Series, Volume 10, at 71-87 (1986).
An exemplary induction tool is shown in the prior art drawing of FIG. 1, in which one or more transmitters (T) and a plurality of receivers (Ri) are shown in a logging sonde. Each transmitter or receiver may be a set of coils, with modern array induction tools having several receivers, e.g. R1, R2, R3, and R4, of increasing transmitter-to-receiver spacing to measure progressively deeper into the formation.
In a conventional induction tool such as that shown in FIG. 1, the coils are wound coaxially around a cylindrical mandrel. Both transmitter coils and receiver coils are solenoidal, and are wound coaxial with the mandrel. Such coils would therefore be aligned with the principal axis of the logging tool, which is normally also the central axis of the borehole and is usually referred to as the z-axis. That is, the magnetic moments of the coils are aligned with the axis of the mandrel on which they are wound. The number, position, and numbers of turns of the coils are arranged to null the signal in a vacuum due to the mutual inductance of transmitters and receivers.
During operation, an oscillator supplies alternating current to the transmitter coil or coils, thereby inducing current in the receiver coil or coils. The voltage of the current induced in the receiver coils results from the sum of all eddy currents induced in the surrounding formations by the transmitter coils. Phase sensitive electronics measure the receiver voltage that is in-phase with the transmitter current divided by magnitude of the transmitter current. When normalized with the proper scale factor, this provides signals representing the apparent conductivity of that part of the formation through which the transmitted signal passed. The out-of-phase, or quadrature, component can also be useful because of its sensitivity to skin effect although it is less stable and is adversely affected by contrasts in the magnetic permeability.
As noted, the induced eddy currents tend to flow in circular paths that are coaxial with the transmitter coil. As shown in FIG. 1, for a vertical borehole traversing horizontal formations, there is a general symmetry for the induced current around the logging tool. In this ideal situation, each line of current flow remains in the same formation along its entire flow path, and never crosses a bed boundary.
In many situations, as shown for example in FIG. 2, the wellbore is not vertical and the bed boundaries are not horizontal. The well bore in FIG. 2 is shown with an inclination angle θ measured relative to true vertical. A bed boundary between formations is shown with a dip angle α. The inclined wellbore strikes the dipping bed at an angle β. As a result, the induced eddy currents flow through more than one media, encountering formations with different resistive properties. The resulting logs are distorted, especially as the dip angle α of the bed boundaries increases. If the logging tool traverses a thin bed, the problem becomes even more exaggerated.
As shown in the graph of FIG. 3A, an induction sonde traversing a dipping bed produces a log with distortions normally referred to as “horns”. The more severe the dip angle, the less accurate is the measurement with depth. FIG. 3A represents a computer simulation of a log that would be generated during logging of a ten-foot thick bed (in actual depth), with different plots for different dip angles. FIG. 3B shows a computer simulation of a log which would be generated if the thickness of the bed were true vertical depth, with different plots for different dip angles. As is evident from these simulated logs, as the dip angle increases, the accuracy and meaningfulness of the log decreases. In instances of high dip angles, the plots become virtually meaningless in the vicinity of the bed boundaries.
FIGS. 3A and 3B also illustrate that even for a vertical well traversing horizontal formations, the actual electrical signal or data produced by an induction logging tool is quite different from an exact plot of formation resistivities. In these figures the desired representations of formation resistivity are the dashed line square wave shapes 10 and 20. The actual resistivity within a layer is generally uniform so that there are abrupt changes in resistivity at the interfaces between layers. However, logging tools have limited resolution and do not directly measure these abrupt changes. When the transmitter coil T in FIG. 1 is near an interface, as illustrated, its transmitted signal is split between layers of differing resistivity. As a result, the raw data or signal from the logging tool is a composite or average of the actual values of the adjacent layers. This effect is referred to as the shoulder effect. Even in the 0° case shown in the FIGS. 3A and 3B, where the tool is vertical and the formation is horizontal, the measured data is quite different from the desired representation of resistivity. As the dip increases, the effect is increased.
Much work has been done on methods and equipment for processing logging tool data or signals to produce an accurate representation of formation parameters. This data processing process is commonly called inversion. Inversion is usually carried out in some type of computer. In the prior art system of FIG. 1, a block labeled “computing module” may perform some type of inversion process. The methods currently available to perform this processing are iterative in nature. The standard iterative methods have the disadvantage of being computationally intensive. As a result, the inversion must normally be carried out at computing centers using relatively large computers, which can deliver results of the inversion in a reasonable amount of time, and normally cannot be performed in computers suitable for use at the well site.
An alternative processing method is the deconvolution method. This method is very fast and can be implemented at the well site, for example in the computing module of FIG. 1. However, this method is based on linear filter theory, which is an approximation that is not always accurate. In deviated boreholes, the nonlinearity of the tool response becomes manifest, making the problem hard for the deconvolution method to handle. The deconvolution methods do not generate actual representations of the formation parameters, so they cannot be properly called inversion methods.
Early attempts to solve the inversion of log data problem used the parametric inversion method. This method is an iterative method that uses a forward solver and criteria, such as the least square inversion, to determine the best fit for the parameters of a predefined formation, usually a model with a step profile. However, if the actual formation does not conform to the predefined model, the output parameters determined by this method can be very far from the actual parameters of the formation. This is a consequence of the ill posed nature of the inversion problem which makes it highly non-trivial.
A more current method for inversion of resistivity log data is the Maximum Entropy Method, MEM. In this iterative method, a test or proposed formation model is modified to maximize the entropy functional, which depends on the parameters of the formation. This method does not use a predefined formation and produces solutions of better quality. It is more efficient than the parametric approaches, but is still computationally intensive. It can be applied to any type of tool for which a forward solver is available. An example of the MEM method is disclosed in U.S. Pat. No. 5,210,691 entitled “Method and Apparatus for Producing a More Accurate Resistivity Log from Data Recorded by an Induction Sonde in a Borehole.”
In general, all of the iterative inversion schemes have essentially two parts. The first part is a forward solver that generates a synthetic log from a synthetic test formation which is a reasonable representation of a real formation. The test formation is an assumed generally square wave plot of a formation parameter, e.g. resistivity, versus depth, like the plots 10 and 20 in FIGS. 3A and 3B. The forward solver simulates the response of a selected logging tool to the test formation to generate the synthetic log. If the logging tool has multiple transmitter receiver sets or arrays, as illustrated in FIG. 1, a separate forward solution is needed for each set, since each set responds differently. The second part of the iterative method is a criterion to modify the test formation. The criterion is based on the difference between the synthetic log corresponding to the test formation and the real log data measured by the tool. After the test formation has been modified, a new synthetic log is generated by the forward solver. This process is repeated iteratively until the difference between the synthetic log and the real log is less than a predefined tolerance. The output of the inversion algorithm is the parameters of the final test formation. These parameters are plotted versus depth to produce the desired log. It is the iterative nature of these methods which makes them computationally intensive.
Various efforts have been made to use Artificial Neural Networks, ANN, as part of inversion processes. For example, in the paper entitled “Detection of Layer Boundaries from Array Induction Tool Responses using Neural Networks”, 69th Annual SEG international meeting (Houston, 1999). Expanded abstract, V1, pp 140-143, the authors Srinivasa V. Chakravarthy, Raghu K. Chunduru, Alberto G. Mezzatesta, and Otto Fanini use a trained radial basis function neural network to identify bed boundaries from induction well logs. The network is trained using the logarithmic derivative of both measured and synthetic log data. As a result, actual log data to be processed by the trained neural network must also be first processed by taking the logarithmic derivative. The detected bed boundaries are then used in known inversion processes.
In the publication entitled “Artificial Neural Networks And High Speed Resistivity Modeling Software Speeds Reservoir Characterization”, Jeff S. Arbogast and Mark H. Franklin, Petroleum Engineer International, pp. 57-61, the authors describe use of a neural network trained on real well logs of various types. By proper selection of available logs for training, it is reported that it is possible to synthesize missing logs or fill in bad data for other wells in the same field.
In U.S. Pat. No. 5,251,286, Method for Estimating Formation Permeability from Wireline Logs Using Neural Networks, the inventors Jacky M. Wiener, Robert F. Moll and John A. Rogers disclose use of a neural network to determine permeability. The network is trained with resistivity, neutron porosity, bulk density, interval transit time, and other logs and actual measured core permeability. It is then able to use the same wireline log measurements from other wells in the same area to estimate formation permeability in wells from which cores were not actually taken and measured.
In U.S. Pat. No. 5,862,513, Systems and Methods for Forward Modeling of Well Logging Tool Responses, the inventors Alberto G. Mezzatesta, Michael A. Jervis, David R. Beard, Kurt M. Strack, and Leonty A. Tabarovsky disclose use of a neural network to produce synthetic tool responses for a well logging tool. The neural network is trained to simulate the response of a particular logging tool to models of earth formations. The trained network is intended for use as the forward solver in an iterative inversion process.
In U.S. Pat. No. 6,044,325, Conductivity Anisotropy Estimation Method for Inversion Processing of Measurements Made by a Transverse Electromagnetic Induction Logging Instrument, the inventors Srinivasa V. Chakravarthy, Pravin Gupta, Raghu Chunduru, Berthold G. Kriegshauser, and Otto N. Fanini teach a method of using a trained neural network for improving initial estimates of formation parameters. The network is trained by first synthesizing the response of the tool to models of earth formations. Then initial estimates of the earth parameters are calculated from the synthesized responses. The initial estimates and known earth models are used to train a neural network. To use the trained network with real data, actual tool signals are first processed to produce an initial estimate of earth parameters. These processed signals are then input to the trained neural network to produce improved estimates of parameters.
While these references have shown improvements in well log inversion by use of trained neural networks, none of them have taught a method for direct inversion of logging tool signals to produce a log of formation parameters. Direct inversion would be faster than the prior art methods and would allow real time generation of well logs at the well site. It would also allow real time processing of logging tool signals in LWD or MWD. This would be quite useful to the drilling engineer during the drilling process. For example, in slant well drilling the well logs could be used in guiding the drilling system.